Page push method, device, computer equipment, readable storage medium and program product

Abstract

The present application relates to a page push method, apparatus, computer device, computer-readable storage medium, and computer program product. The method comprises: after a target user logs in to a push application, determining at least one historical page visited by the target user during a target historical period; determining multiple similar users of the target user during the target historical period, and using at least one historical page visited by each similar user and at least one historical page visited by the target user during the target historical period as pages to be filtered; for each page to be filtered, determining the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and determining a target page based on the interest in each page to be filtered, storing the page data of the target page as hotspot data in a local cache; and upon receiving a page access request from the target user, determining the target hotspot data from the local cache for page push, thereby improving the push effect.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to a page push method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Art

[0002] With the rapid development of Internet technology, the rapid expansion of business models and the increasing user needs, the amount of data and user access to Web (network) applications has increased dramatically, and traditional database access methods can no longer meet access needs.

[0003] In related technologies, a unified caching strategy is used to store and reuse previously requested push data to reduce database pressure. However, these caching strategies typically focus on hot data across the entire push system, failing to provide personalized page pushes and resulting in poor page push performance. Summary of the Invention

[0004] Based on this, it is necessary to provide a page push method, device, computer equipment, computer-readable storage medium and computer program product that can provide personalized page push and improve the page push effect in response to the above technical problems.

[0005] In a first aspect, the present application provides a page push method, comprising:

[0006] After the target user logs in to the push application, determining at least one historical page visited by the target user within a target historical period;

[0007] Determine multiple similar users who have similar page preferences as the target user during the target historical period, and use at least one historical page visited by each similar user during the target historical period and at least one historical page visited by the target user during the target historical period as pages to be filtered;

[0008] For each page to be filtered, determine the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filter out a target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and store the page data of the target page as hot data in the local cache;

[0009] After receiving the page access request of the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and page push is performed based on the target hotspot data.

[0010] In a second aspect, the present application further provides a page pushing device, comprising:

[0011] A page determination module, configured to determine at least one historical page visited by a target user within a target historical period after the target user logs into the push application;

[0012] A user determination module is configured to determine a plurality of similar users who have similar page preferences as the target user within the target historical period, and to use at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be screened;

[0013] a page screening module configured to determine, for each page to be screened, the target user's interest in the page to be screened based on the behavioral similarity between each similar user and the target user, and to screen a target page from at least one page to be screened based on the interest levels corresponding to the pages to be screened, and to store the page data of the target page as hotspot data in a local cache;

[0014] The page push module is used to determine target hotspot data based on at least one hotspot data in the local cache after receiving a page access request from the target user, and push the page based on the target hotspot data.

[0015] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0016] After the target user logs in to the push application, determining at least one historical page visited by the target user within a target historical period;

[0017] Determine multiple similar users who have similar page preferences as the target user during the target historical period, and use at least one historical page visited by each similar user during the target historical period and at least one historical page visited by the target user during the target historical period as pages to be filtered;

[0018] For each page to be filtered, determine the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filter out a target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and store the page data of the target page as hot data in the local cache;

[0019] After receiving the page access request of the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and page push is performed based on the target hotspot data.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0021] After the target user logs in to the push application, determining at least one historical page visited by the target user within a target historical period;

[0022] Determine multiple similar users who have similar page preferences as the target user during the target historical period, and use at least one historical page visited by each similar user during the target historical period and at least one historical page visited by the target user during the target historical period as pages to be filtered;

[0023] For each page to be filtered, determine the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filter out a target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and store the page data of the target page as hot data in the local cache;

[0024] After receiving the page access request of the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and page push is performed based on the target hotspot data.

[0025] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0026] After the target user logs in to the push application, determining at least one historical page visited by the target user within a target historical period;

[0027] Determine multiple similar users who have similar page preferences as the target user during the target historical period, and use at least one historical page visited by each similar user during the target historical period and at least one historical page visited by the target user during the target historical period as pages to be filtered;

[0028] For each page to be filtered, determine the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filter out a target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and store the page data of the target page as hot data in the local cache;

[0029] After receiving the page access request of the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and page push is performed based on the target hotspot data.

[0030] The above-mentioned page push method, apparatus, computer equipment, computer-readable storage medium and computer program product, after the target user logs in to the push application, determines at least one historical page visited by the target user within the target historical period; determines multiple similar users with similar page preferences as the target user within the target historical period, and uses at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be filtered; for each page to be filtered, determines the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filters out the target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and stores the page data of the target page as hot data in the local cache. That is, after the pages of interest to the target user are filtered out, they are stored in the local cache in advance. In this way, after receiving the page access request from the target user, there is no need to spend a lot of time waiting for data to be loaded, thereby reducing access delay. Based on at least one hot spot data in the local cache, the target hot spot data that the target user is interested in can be quickly queried, and personalized page push can be performed in a timely and fast manner based on the target hot spot data. On the premise of improving the push speed, accurate push can be performed for the target user to improve the page push effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is an application environment diagram of a page push method in one embodiment;

[0033] Figure 2 Schematic diagram of a flow chart of a page push method in one embodiment;

[0034] Figure 3 A schematic diagram of a sliding window determination step in one embodiment;

[0035] Figure 4 This is a schematic diagram of the interaction between various units when pushing a page in an embodiment;

[0036] Figure 5 A schematic diagram of page push steps in one embodiment;

[0037] Figure 6 This is a structural block diagram of a page pushing device in one embodiment;

[0038] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0040] Before introducing the embodiments of the present application, the following terms are explained:

[0041] Memcached: It is a memory-based key-value storage system and a high-performance distributed memory cache system used to store small blocks of arbitrary data (such as strings and objects). It is mainly used to accelerate database access speed of web applications by caching data, reduce database load, and improve data retrieval efficiency.

[0042] Pearson Correlation Coefficient: It is widely used to measure the degree of correlation between two variables, and its value is between -1 and 1.

[0043] Sliding window: A method widely used in data stream processing, real-time analysis, and cache management. It tracks and processes the most recent data items by maintaining a fixed-size window on the data set.

[0044] The page push method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be integrated on the server 104 or placed on the cloud or other network servers.

[0045] In some embodiments, after the target user logs in to the push application, the terminal 102 obtains at least one historical page visited by the target user within the target historical period sent by the server 104; the terminal 102 determines multiple similar users who have similar page preferences with the target user within the target historical period, and uses at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be filtered; for each page to be filtered, the target user's interest in the page to be filtered is determined based on the behavioral similarity between each similar user and the target user, and based on the corresponding interest level of each page to be filtered, the target page is filtered out from at least one page to be filtered, and the page data of the target page is stored as hot spot data in the local cache; after receiving the page access request from the target user, the terminal 102 determines the target hot spot data based on at least one hot spot data in the local cache, and pushes the page based on the target hot spot data.

[0046] The local cache in terminal 102 may be a Memcached cache. A push system is deployed on server 104, which can be understood as a push server. Terminal 102 may be, but is not limited to, various personal computers, laptops, smartphones, tablets, etc. Server 104 may be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0047] In an exemplary embodiment, Figure 2 As shown, a page push method is provided, which is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate the process, including the following steps S202 to S208.

[0048] Step S202: After the target user logs in to the push application, at least one historical page visited by the target user within a target historical period is determined.

[0049] Among them, the push application refers to an application that can push content, and the history page is a push page in the push application.

[0050] For example, the terminal obtains historical user behavior data of the target user during a target historical period from the server. The historical user behavior data is browsing data since the target user logged into the push application. The terminal parses the historical user behavior data to determine at least one historical page visited by the target user during the target historical period.

[0051] Step S204, determine multiple similar users who have similar page preferences as the target user within the target historical period, and use at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be filtered.

[0052] Among them, similar users refer to users whose web page behaviors are similar to those of the target user during the target historical period, and can be understood as other users with similar page preferences.

[0053] Optionally, the terminal obtains login data about the push application within the target historical period from the server, obtains other users who have logged into the push application within the target historical period from the login data, and obtains historical user behavior data of each other user from the login data.

[0054] The terminal identifies multiple similar users from the other users based on the historical user behavior data of the target user and the historical user behavior data of each other user. The terminal uses at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be filtered.

[0055] Exemplarily, the terminal determines the behavioral similarity between the target user and other users through similarity calculation (such as the Pearson correlation coefficient) based on the historical user behavior data of the target user and the historical user behavior data of each other user, and determines multiple similar users based on the corresponding behavioral similarities of each other user.

[0056] In other embodiments, after determining multiple similar users who have similar page preferences as the target user during the target historical period, the method further includes: using the multiple similar users who have similar page preferences as the target user during the target historical period as pages to be filtered. Then, the following steps S206-208 are executed.

[0057] In step S206, for each page to be filtered, the target user's interest level in the page to be filtered is determined based on the behavioral similarity between each similar user and the target user, and the target page is filtered out from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and the page data of the target page is stored as hot data in the local cache.

[0058] Among them, the higher the behavioral similarity between the target user and the similar users, the more similar the page preferences of the two users are.

[0059] Exemplarily, for each page to be filtered, the terminal determines the target user's interest level in the page to be filtered based on the behavior similarity between the target user and each similar user, and the historical user behavior data of each similar user.

[0060] The terminal sorts the pages by interest level from high to low, and selects a preset number of top-ranked pages to be screened as target pages. The terminal obtains the page data of the target page returned by the server, uses the page data of the target page as hot data, and stores the hot data in a local cache in correspondence with the page ID of the target page and the target user ID.

[0061] Step S208: After receiving the page access request from the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and the page is pushed based on the target hotspot data.

[0062] Exemplarily, in response to a page access operation, one or more hotspot data are randomly selected from at least one hotspot data corresponding to a target user in a local cache as target hotspot data, and a page is pushed based on the target hotspot data.

[0063] In the above page push method, after the target user logs in to the push application, at least one historical page visited by the target user within the target historical period is determined; multiple similar users with similar page preferences as the target user within the target historical period are determined, and at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period are used as pages to be filtered; for each page to be filtered, the target user's interest in the page to be filtered is determined based on the behavioral similarity between each similar user and the target user, and based on the interest corresponding to each page to be filtered, the target page is filtered out from at least one page to be filtered, and the page data of the target page is stored as hot data in the local cache. That is, after the page of interest to the target user is filtered out, it is stored in the local cache in advance. In this way, after receiving the page access request of the target user, there is no need to spend a lot of time waiting for data to be loaded, thereby reducing access delay. Based on the at least one hot data in the local cache, the target hot data of interest to the target user can be quickly queried, and personalized page push is performed promptly and quickly based on the target hot data. On the premise of improving the push speed, accurate push is performed for the target user to improve the page push effect.

[0064] In some embodiments, the target historical period determination step includes: counting the total historical period of the target user logging into the push application at the current moment, and obtaining the total historical user behavior data of the target user within the total historical period, and obtaining the total historical system load data about the push system within the total historical period; based on the total historical user behavior data and the total historical system load data, determining the sliding window corresponding to the target user; sliding the sliding window at least once in the total historical period, and taking the period corresponding to the last slide as the target historical period.

[0065] The total historical period is the total login time of the target user. It can be the period between the first login and the most recent login time, or a period selected by the user. The total historical period is greater than or equal to the target historical period.

[0066] The total historical system load data reflects the load situation in the push system during the total historical period and is used to detect performance issues. The total historical system load data includes at least CPU (Central Processing Unit) usage, memory usage, and disk I / O (input / output).

[0067] The total historical user behavior data reflects the historical access situation of a certain user within the total historical period. The total historical user behavior data at least includes page access frequency and user stay time.

[0068] In this embodiment, by using historical user behavior data and historical system load data within a historical time period, a sliding window is used to determine the most appropriate duration closest to the current moment from the perspective of page interaction and load, ensuring the effectiveness and timeliness of subsequent caching. In other words, the sliding window intelligently updates cached hotspot data, ensuring that only the latest or most interesting data is pushed to users, ensuring the timeliness of page push and improving the user experience.

[0069] In some embodiments, as Figure 3 FIG. 1 is a schematic diagram of a sliding window determination step in one embodiment. Based on the total historical user behavior data and the total historical system load data, the sliding window corresponding to the target user is determined, including:

[0070] Step S302: Based on the total historical user behavior data, the target user's target page access frequency and target page dwell time are determined, and the sliding window size is determined according to the target page access frequency, target page dwell time and the total historical system load data.

[0071] Exemplarily, the target page access frequency and target page dwell time of the target user within the total historical period are obtained from the total historical user behavior data. For example, the sum of the page access frequencies of each historical page visited by the target user within the total historical period is added together to serve as the target page access frequency of the target user within the total historical period. Alternatively, the maximum page access frequency is selected from the page access frequencies of at least one historical page visited by the target user to serve as the target page access frequency. For example, the sum of the page dwell time of each historical page visited by the target user within the total historical period is added together to serve as the target page dwell time of the target user within the total historical period. Alternatively, the longest page dwell time is selected from the page dwell time of at least one historical page visited by the target user to serve as the target page dwell time.

[0072] For example, the terminal obtains one of CPU usage, memory usage, and disk I / O usage from historical system load data. The sliding window size is determined by combining the target page access frequency and target page dwell time within the historical total period with the obtained usage rate. For example, the sliding window size is determined by superimposing the obtained usage rate, the target page access frequency, and the target page dwell time.

[0073] Step S304: obtaining the current processing capacity of the push system at the current moment, and determining the adjustment factor corresponding to the current moment based on the current system load data and the total historical system load data of the push system.

[0074] The push system's processing capacity is correlated with load; a greater load results in a lower processing capacity. The adjustment factor adjusts the ratio between the current processing capacity and the maximum processing capacity. Load refers to the load on the push system, represented by at least one of the aforementioned CPU utilization, memory utilization, and disk I / O utilization. Current system load data is the current load data for the push system.

[0075] For example, the adjustment factor is calculated using the following formula (1): :

[0076] (1)

[0077] Among them, e is the exponential operation, S is the target page access frequency in the total historical system load data, is the target page access frequency in the current system load data. S is the target page stay time in the total historical system load data. The duration of the target page in the current system load data.

[0078] Step S306: Determine the sliding step size based on the maximum processing capacity, current processing capacity and adjustment factor of the push system.

[0079] For example, the terminal calculates the current processing capacity and maximum processing capacity and the adjustment factor The product of is taken as the sliding step length L. For example, referring to formula (2):

[0080] (2)

[0081] It's important to note that the sliding step size, L, is set based on the push system's processing power and real-time requirements. This determines the frequency of sliding window updates, ensuring that data processing does not exceed the push system's load and that the push system can respond promptly to data changes. A smaller L means more frequent updates, potentially leading to higher push system load. Therefore, the sliding step size, L, should be inversely proportional to the push system's load.

[0082] Step S308: Determine the sliding window based on the sliding window size and the sliding step size.

[0083] In this embodiment, the current sliding window is adaptively adjusted based on the push system's load to ensure that determining hot data within the currently defined sliding window does not significantly impact the push system's load, thus ensuring the reliability of page push. In other words, the sliding window can subsequently intelligently update cached data, ensuring that only the latest or most interesting data is pushed to the user. This also improves cache efficiency, ensuring optimal use of cache space and more efficient utilization of system resources.

[0084] In some embodiments, the sliding window size is determined based on the target page access frequency, the target page dwell time and the total historical system load data, including: weighting the target page access frequency, the target page dwell time and the total historical system load data according to their respective corresponding weights to obtain the sliding window size.

[0085] For example, a usage rate is selected from the three usage rates of CPU usage, memory usage, and disk I / O usage in the historical system load data. The target page access frequency, target page dwell time, and the selected usage rate are weighted according to their respective weights to determine the sliding window size.

[0086] For example, the following formula (3) is used to determine the sliding window size: :

[0087] (3)

[0088] in, 、 and is the weight coefficient, which is used to adjust the influence of different factors on the window size. is the target page visit frequency in the total historical period; t is the target user's stay time in the total historical period, This is historical load data.

[0089] It should be noted that the size of the sliding window determines the amount of data that is captured to sufficiently represent user behavior. The larger the window, the more data is included in the processing.

[0090] In this embodiment, by weighted summing the target page access frequency, the target page dwell time and the total historical system load data, the three indicators of page access frequency, dwell time and load can be integrated to obtain a more accurate sliding window size.

[0091] In some embodiments, determining multiple similar users who have similar page preferences as the target user within the target historical period includes: determining multiple other users who logged into the push application within the target historical period; for each other user, calculating the behavioral similarity between the other user and the target user based on the historical user behavior data of the other user and the historical user behavior data of the target user; sorting the multiple behavioral similarities from high to low, and taking the other users corresponding to a preset number of behavioral similarities that are ranked at the top as similar users of the target user.

[0092] Exemplarily, for each other user and each page to be filtered, based on the historical user behavior data of the other users, the sub-historical user behavior data of the other users for the page to be filtered is obtained to determine the page access frequency of the other users to the page to be filtered; based on the sub-historical user behavior data of the target user for the page to be filtered, the page access frequency of the target user to the page to be filtered is determined.

[0093] For each other user and each to-be-screened page, the behavioral similarity between the other user and the target user is calculated using the Pearson correlation coefficient function based on the page access frequency of the other user accessing the to-be-screened page and the page access frequency of the target user accessing the to-be-screened page.

[0094] Alternatively, for each other user and each page to be filtered, the length of time the other user stays on the page to be filtered is determined based on the sub-historical user behavior data of the other user for the page to be filtered; and the length of time the target user stays on the page to be filtered is determined based on the sub-historical user behavior data of the target user for the page to be filtered.

[0095] For each other user and each page to be filtered, the behavioral similarity between the other user and the target user is calculated using the Pearson correlation coefficient function based on the page dwell time of the other user when visiting the page to be filtered and the page dwell time of the target user when visiting the page to be filtered.

[0096] For example: For each other user v, refer to the following formula (4) to determine the behavioral similarity between the other user v and the target user u: :

[0097] (4)

[0098] Where n is the number of pages to be filtered. is the page access frequency of target user u to the filtered page i during the target historical period. is the average page access frequency of the target user u in the target historical period, that is, the average of the sum of the page access frequencies of the target user u visiting each page to be filtered. is the page visit frequency or page stay time of the target user v on the filtered page i during the target historical period. The average page access frequency of the target user v during the target historical period, that is, the average of the sum of the page access frequencies of the target user v visiting each page to be filtered.

[0099] Of course, the page access frequency can also be replaced by the page dwell time in the above formula (3) for calculation.

[0100] In this embodiment, by using the historical user behavior data of other users and the historical user behavior data of the target user, it is possible to more accurately assess whether the other users have similar page preferences to the target user.

[0101] In some embodiments, the target user's interest level in the page to be filtered is determined based on the behavioral similarity between each similar user and the target user, including: for each similar user, obtaining sub-historical user behavior data about the page to be filtered from the historical user behavior data of the similar user, and determining the sub-interest level corresponding to the similar user based on the behavioral similarity between the similar user and the target user, as well as the sub-historical user behavior data; and fusing the sub-interest levels corresponding to each similar user to determine the target user's interest level in the page to be filtered.

[0102] For example, for each similar user, the terminal obtains the page access frequency from the similar user's sub-historical user behavior data for the page to be filtered. Based on the behavioral similarity between the similar user and the target user and the obtained page access frequency, the terminal determines the sub-level of interest corresponding to the similar user. After fusing and normalizing the sub-levels of interest corresponding to each similar user, the target user's level of interest in the page to be filtered is obtained.

[0103] Alternatively, for each similar user, the terminal obtains the page dwell time from the similar user's sub-historical user behavior data for the page to be filtered. Based on the behavioral similarity between the similar user and the target user and the obtained page dwell time, the terminal determines the sub-level of interest corresponding to the similar user. After fusing and normalizing the sub-levels of interest corresponding to each similar user, the target user's level of interest in the page to be filtered is obtained.

[0104] The following example illustrates this. See formula (5) for details. This is to calculate the interest level of the target user u in the filtered page P. :

[0105] (5)

[0106] in, A collection of similar users to the target user. is the behavior similarity between target user u and similar user v.

[0107] In this embodiment, the sub-historical user behavior data of similar users and the behavioral similarity between similar users and the target user are used to accurately predict the target user's interest in the corresponding page to be filtered, so that the target page can be filtered out and stored in the local cache in time, thereby improving the page loading efficiency.

[0108] In a specific embodiment, Figure 4 The figure shows the interaction between the various units when pushing a page in one embodiment. Figure 4 The terminal includes a user behavior detection unit, a system load detection unit, a data processing center, a cache strategy adjustment unit, a data pre-fetching unit and a local cache. Figure 5 As shown in FIG. 1 , a schematic diagram of the page push step in an embodiment is shown. Figure 4 and Figure 5 The page push process provided by the embodiment of this application is described as follows:

[0109] Step 1: Determine the sliding window corresponding to the target user.

[0110] Specifically, when a target user logs into the push application, the user behavior detection unit obtains the target user's total historical user behavior data for the target historical time period and sends it to the data processing center. The system load detection unit also obtains the total historical system load data for the push system during the total historical time period and sends it to the data processing center. Based on the obtained total historical user behavior data and the total historical system load data of the push system, the data processing center determines the sliding window corresponding to the target user.

[0111] Exemplarily, the target user's total historical user behavior data and total historical system load data can be obtained from the server, or by the user behavior detection unit and the system load detection unit, respectively. For example, the user behavior detection unit is configured with JavaScript (a scripting language), which enables querying page access frequency and user dwell time. Using a browser to record the pages visited by each user and their timestamps, Ajax technology (a web development technology used to create interactive web applications) is used to trigger data transmission to the server periodically or when the user leaves the page. Web analytics tools are integrated to automatically query the number of page visits and provide detailed analysis reports, thereby enabling page access frequency queries. To query user dwell time, a timer is set when the page loads to periodically check user activity. Event detectors are added to interactive elements on the page (such as buttons, links, forms, etc.). By listening for events, the moment when the user leaves the page is captured and the dwell time is calculated.

[0112] The system load detection unit collects performance data of user devices and monitors the operation status of web applications (push applications) through the API (application programming interface) provided by the browser, and uses a chart library on the front end to visualize performance data.

[0113] Exemplarily, the data processing center pre-processes the total historical user behavior data and the total historical system load data.

[0114] Next, the cache strategy adjustment unit determines a sliding window based on the pre-processed historical user behavior total data and historical system load total data, and determines a sliding window corresponding to the target user.

[0115] For example, based on the total historical user behavior data, the target page visit frequency and target page stay time of the target user are determined, and the target page visit frequency, target page stay time and historical system load total data are weighted according to their corresponding weights to obtain the sliding window size; the current processing capacity of the push system at the current moment is obtained, and based on the current system load data of the push system and the historical system load total data, the adjustment factor corresponding to the current moment is determined; based on the maximum processing capacity of the push system, the current processing capacity and the adjustment factor, the sliding step size is determined; based on the sliding window size and the sliding step size, the sliding window is determined.

[0116] Step 2: The cache policy adjustment unit initializes the sliding window.

[0117] Step 3: The cache strategy adjustment unit slides the total historical period in sequence according to the sliding window to determine the target historical period.

[0118] Specifically, the sliding window is slid at least once in the total historical period, and the period corresponding to the last sliding is used as the target historical period.

[0119] Step 4: The data pre-fetching unit analyzes the target pages that the target user is interested in based on the historical user behavior data of similar users of the target user and the historical user behavior data of the target user.

[0120] Specifically, multiple other users who logged into the push application within a target historical period are identified; for each other user, the behavioral similarity between the other user and the target user is calculated based on the historical user behavior data of the other user and the historical user behavior data of the target user; the multiple behavioral similarities are ranked from high to low, and the top-ranked preset number of other users corresponding to the behavioral similarities are each considered similar users to the target user. At least one historical page visited by each similar user within the target historical period, and at least one historical page visited by the target user within the target historical period, are both considered as pages to be filtered.

[0121] For each page to be filtered and each similar user, the sub-historical user behavior data for the page to be filtered is obtained from the historical user behavior data of the similar user. Based on the behavioral similarity between the similar user and the target user and the sub-historical user behavior data, the sub-interest level corresponding to the similar user is determined. The sub-interest levels corresponding to each similar user are combined to determine the target user's interest level in the page to be filtered. Based on the interest levels corresponding to each page to be filtered, the target page is filtered out from at least one page to be filtered.

[0122] Step 5: The local cache uses the target page's page data as hot data and loads the hot data into the cache. After receiving the target user's page access request, the local cache determines the target hot data based on at least one hot data in the local cache, and pushes the page based on the target hot data.

[0123] In this embodiment, after the target user logs in to the push application, at least one historical page visited by the target user within the target historical period is determined; multiple similar users with similar page preferences as the target user within the target historical period are determined, and at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period are used as pages to be filtered; for each page to be filtered, the target user's interest in the page to be filtered is determined based on the behavioral similarity between each similar user and the target user, and based on the interest corresponding to each page to be filtered, the target page is filtered out from at least one page to be filtered, and the page data of the target page is stored as hot data in the local cache. That is, after the page of interest to the target user is filtered out, it is stored in the local cache in advance. In this way, after receiving the page access request of the target user, there is no need to spend a lot of time waiting for data to be loaded, thereby reducing access delay. Based on the at least one hot data in the local cache, the target hot data of interest to the target user can be quickly queried, and personalized page push is performed promptly and quickly based on the target hot data. On the premise of improving the push speed, accurate push is performed for the target user to improve the page push effect, thereby significantly improving the user experience.

[0124] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0125] Based on the same inventive concept, the embodiments of the present application also provide a page push device for implementing the page push method involved above. The implementation solution provided by this device is similar to the implementation solution described in the above method. Therefore, the specific limitations of one or more embodiments of the page push device provided below can be referred to the limitations of the page push method above and will not be repeated here.

[0126] In an exemplary embodiment, Figure 6 As shown, a page pushing device 600 is provided, comprising: a page determination module 602, a user determination module 604, a page screening module 606 and a page pushing module 608, wherein:

[0127] A page determination module 602 is configured to determine at least one historical page visited by the target user within a target historical period after the target user logs into the push application;

[0128] The user determination module 604 is configured to determine multiple similar users who have similar page preferences as the target user within a target historical period, and to use at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be filtered;

[0129] The page screening module 606 is configured to determine, for each page to be screened, the target user's interest level in the page to be screened based on the behavioral similarity between each similar user and the target user, and to screen the target page from at least one page to be screened based on the interest level corresponding to each page to be screened, and store the page data of the target page as hot data in the local cache;

[0130] The page push module 608 is used to determine target hotspot data based on at least one hotspot data in the local cache after receiving a page access request from a target user, and push the page based on the target hotspot data.

[0131] In some embodiments, the device also includes a time period determination module, which is used to count the total historical time period of the target user logging into the push application at the current moment, and obtain the total historical user behavior data of the target user within the total historical time period, and obtain the total historical system load data about the push system within the total historical time period; based on the total historical user behavior data and the total historical system load data, determine the sliding window corresponding to the target user; slide the sliding window at least once in the total historical time period, and use the time period corresponding to the last slide as the target historical time period.

[0132] In some embodiments, a time period determination module is used to determine the target user's target page access frequency and target page dwell time based on the total historical user behavior data, and determine the sliding window size based on the target page access frequency, target page dwell time and the total historical system load data; obtain the current processing capacity of the push system at the current moment, and determine the adjustment factor corresponding to the current moment based on the current system load data of the push system and the total historical system load data; determine the sliding step size based on the maximum processing capacity, current processing capacity and adjustment factor of the push system; determine the sliding window based on the sliding window size and the sliding step size.

[0133] In some embodiments, the time period determination module is used to weight the target page access frequency, target page dwell time and historical system load total data according to their respective corresponding weights to obtain the sliding window size.

[0134] In some embodiments, the user determination module 604 is used to determine multiple other users who logged into the push application within the target historical period; for each other user, based on the historical user behavior data of the other user and the historical user behavior data of the target user, calculate the behavioral similarity between the other user and the target user; sort the multiple behavioral similarities from high to low, and use the other users corresponding to a preset number of behavioral similarities that are ranked at the top as similar users of the target user.

[0135] In some embodiments, the page filtering module 606 is used to obtain, for each similar user, sub-historical user behavior data about the page to be filtered from the historical user behavior data of the similar user, and determine the sub-interest level corresponding to the similar user based on the behavioral similarity between the similar user and the target user, as well as the sub-historical user behavior data; and integrate the sub-interest levels corresponding to each similar user to determine the target user's interest level in the page to be filtered.

[0136] Each module in the above-mentioned page push device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0137] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a page push method is implemented.

[0138] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0139] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0140] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0141] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0142] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0143] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, artificial intelligence (AI) processors, and the like.

[0144] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0145] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A page push method, characterized in that: The method comprises: After the target user logs in to the push application, at least one historical page visited by the target user within a target historical period is determined; wherein the step of determining the target historical period includes: determining the target page visit frequency and target page dwell time of the target user based on the total historical user behavior data, and determining the sliding window size according to the target page visit frequency, the target page dwell time and the total historical system load data; obtaining the current processing capacity of the push system at the current moment, and determining the adjustment factor corresponding to the current moment based on the current system load data of the push system and the total historical system load data; determining the sliding step size based on the maximum processing capacity of the push system, the current processing capacity and the adjustment factor; determining the sliding window based on the sliding window size and the sliding step size; sliding the sliding window at least once in the total historical period through the sliding window, and taking the period corresponding to the last slide as the target historical period; Determine multiple similar users who have similar page preferences as the target user during the target historical period, and use at least one historical page visited by each similar user during the target historical period and at least one historical page visited by the target user during the target historical period as pages to be filtered; For each page to be filtered, determine the target user's interest in the page to be filtered based on the behavioral similarity between each similar user and the target user, and filter out a target page from at least one page to be filtered based on the interest level corresponding to each page to be filtered, and store the page data of the target page as hot data in the local cache; After receiving the page access request of the target user, target hotspot data is determined based on at least one hotspot data in the local cache, and page push is performed based on the target hotspot data.

2. The method according to claim 1, characterized in that The method further comprises: At the current moment, the total historical period of the target user logging into the push application is counted, and the total historical user behavior data of the target user in the total historical period is obtained, and the total historical system load data of the push system in the total historical period is obtained.

3. The method according to claim 1, characterized in that Determining the sliding window size according to the target page access frequency, the target page stay time, and historical system load total data includes: The target page access frequency, the target page stay time and the historical system load total data are weighted according to their respective corresponding weights to obtain the sliding window size.

4. The method according to claim 1, wherein The determining of a plurality of similar users having similar page preferences as the target user within the target historical period includes: determining a plurality of other users who logged into the push application during the target historical period; For each other user, calculating the behavior similarity between the other user and the target user based on the historical user behavior data of the other user and the historical user behavior data of the target user; The multiple behavior similarities are sorted from high to low, and other users corresponding to a preset number of behavior similarities at the top of the ranking are all regarded as similar users of the target user.

5. The method according to claim 1, wherein Determining the target user's interest in the to-be-screened pages based on the behavioral similarity between each similar user and the target user includes: For each similar user, obtaining sub-historical user behavior data about the to-be-filtered page from the historical user behavior data of the similar user, and determining a sub-interest level corresponding to the similar user based on the behavioral similarity between the similar user and the target user and the sub-historical user behavior data; The sub-interest levels corresponding to the similar users are integrated to determine the target user's interest level in the page to be filtered.

6. A page pushing device, characterized in that: The device comprises: A page determination module is configured to determine at least one historical page visited by a target user within a target historical period after the target user logs into a push application; wherein the device further comprises a period determination module configured to determine the target page visit frequency and target page dwell time of the target user based on the total historical user behavior data, and determine the sliding window size according to the target page visit frequency, the target page dwell time and the total historical system load data; obtain the current processing capacity of the push system at the current moment, and determine the adjustment factor corresponding to the current moment based on the current system load data of the push system and the total historical system load data; determine the sliding step size based on the maximum processing capacity of the push system, the current processing capacity and the adjustment factor; determine the sliding window based on the sliding window size and the sliding step size; slide the sliding window at least once in the total historical period through the sliding window, and use the period corresponding to the last slide as the target historical period; A user determination module is configured to determine a plurality of similar users who have similar page preferences as the target user within the target historical period, and to use at least one historical page visited by each similar user within the target historical period and at least one historical page visited by the target user within the target historical period as pages to be screened; a page screening module configured to determine, for each page to be screened, the target user's interest in the page to be screened based on the behavioral similarity between each similar user and the target user, and to screen a target page from at least one page to be screened based on the interest levels corresponding to the pages to be screened, and to store the page data of the target page as hotspot data in a local cache; The page push module is used to determine target hotspot data based on at least one hotspot data in the local cache after receiving a page access request from the target user, and push the page based on the target hotspot data.

7. The device according to claim 6, characterized in that The user determination module is configured to determine a plurality of other users who logged into the push application within the target historical period; for each other user, based on the historical user behavior data of the other user and the historical user behavior data of the target user, calculate the behavior similarity between the other user and the target user; The multiple behavior similarities are sorted from high to low, and other users corresponding to a preset number of behavior similarities at the top of the ranking are all regarded as similar users of the target user.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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